How Does a Deep Learning Model Architecture Impact Its Privacy?. (arXiv:2210.11049v1 [cs.CR])

As a booming research area in the past decade, deep learning technologies
have been driven by big data collected and processed on an unprecedented scale.
However, the sensitive information in the collected training data raises
privacy concerns. Recent research indicated that deep learning models are
vulnerable to various privacy attacks, including membership inference attacks,
attribute inference attacks, and gradient inversion attacks. It is noteworthy
that the performance of the attacks varies from model to model. In this paper,
we conduct …

architecture deep learning impact privacy